学术科研
Academic research
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Adult children's education and elderly parents' energy poverty: Evidence from China
Adult children's education and elderly parents' energy poverty: Evidence from China
This paper investigates the intergenerational impact of adult children's education on elderly parents' energy poverty. Utilizing data from the China Family Panel Studies (CFPS), we find that higher levels of children's education significantly reduce parents' energy poverty. Our results are robust across various measures of energy poverty and children's education. To address the issue of endogeneity, we employ China's Compulsory Education Law as an instrumental variable for children's education. Heterogeneity tests show that the reduction effect is more pronounced in rural areas compared to urban areas, in families with male children relative to those with female children, in parents with lower levels of education, and in regions with higher educational quality. Intergenerational financial transfers, the digital divide, and social capital are identified as three potential underlying mechanisms through which children's education affects parents' energy poverty. These findings offer a new perspective on the driving forces behind energy poverty, underscoring the significance of intergenerational effects.
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Do investors pay attention to the long-term sustainable company development?
Do investors pay attention to the long-term sustainable company development?
This study investigates whether the green concerns expressed by retail investors on online platforms affect the environmental, social, and governance (ESG) performance of companies in China's A-share market. The implementation of China's dual carbon targets has received significant public support. Investors in the capital market are increasingly emphasizing the green initiatives of the companies they invest in. This creates external pressure on publicly listed firms. Results indicate that retail investors' attention to environmental sustainability significantly enhances companies' ESG performance, primarily by boosting their market reputation. The effect is more pronounced in companies from regions with lower marketization levels, those that are private enterprises, and those in the manufacturing sector.
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Optimal starting point for time series forecasting
Optimal starting point for time series forecasting
Recent advances on time series forecasting mainly focus on improving the forecasting models themselves. However, when the time series data suffer from potential structural breaks or concept drifts, the forecasting performance might be significantly reduced. In this paper, we introduce a novel approach called Optimal Starting Point Time Series Forecast (OSP-TSP) for optimal forecasting, which can be combined with existing time series forecasting models. By adjusting the sequence length via leveraging the XGBoost and LightGBM models, the proposed approach can determine the optimal starting point (OSP) of the time series and then enhance the prediction performances of the base forecasting models. To illustrate the effectiveness of the proposed approach, comprehensive empirical analysis have been conducted on the M4 dataset and other real world datasets. Empirical results indicate that predictions based on the OSP-TSP approach consistently outperform those using the complete time series dataset. Moreover, comparison results reveals that combining our approach with existing forecasting models can achieve better prediction accuracy, which also reflect the advantages of the proposed approach.
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When structural break meets threshold effect: Factor analysis under structural instabilities
When structural break meets threshold effect: Factor analysis under structural instabilities
Structural instability has been one of the central research questions in economics and finance over many decades. This paper systematically investigates structural instabilities in high dimensional factor models, which portray both structural breaks and threshold effects simultaneously. The observed high dimensional time series are concatenated at an unknown number of break points, while they are described by multiple threshold factor models that are heterogeneous between any two consecutive subsamples. Both joint and sequential procedures for estimating the break points are developed based on the second moment of the pseudo factor estimates that fully ignore the structural instabilities. In each separated subsample, the group Lasso approach recently proposed by Ma and Tu (2023b) is adopted to efficiently identify the threshold factor structure. An information criterion is further proposed to determine the number of break points, which also serves the purpose to distinguish the two types of instabilities. Theoretical properties of the proposed estimators are established, and their finite sample performance is evaluated in Monte Carlo simulations. An empirical application to the U.S. financial market dataset demonstrates the consequences when structural break meets threshold effect in factor analysis.
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An opinion evolution-based consensus-reaching model for large-scale group decision-making: Incorporating implicit trust and individual influence
An opinion evolution-based consensus-reaching model for large-scale group decision-making: Incorporating implicit trust and individual influence
Large-scale group decision-making (LGDM) often involves complex challenges, such as effectively clustering decision-makers, modeling asymmetric trust relationships, and balancing the influence of leaders and members to achieve consensus. This study proposes a novel opinion evolution-based consensus-reaching model to address these issues. A convex clustering method is developed, combining the strengths of K-means and hierarchical clustering to enable adaptive subgroup formation and automatic determination of the optimal number of clusters. Anew asymmetric implicit trust measure is developed by combining partnership dynamics with the Pearson Correlation Coefficient, providing a realistic representation of trust relationships. Furthermore, the model identifies leaders and members within each subgroup, quantifies their mutual influence through dynamic weights, and incorporates these dynamics into an improved Friedkin-Johnsen framework to allow for iterative preference adjustments and alignment toward consensus. The feasibility and validity of the proposed method are demonstrated through a case study and sensitivity analysis, highlighting its adaptability and effectiveness. Simulation experiments further validate the model, showing superior performance compared to existing methods.
专题项目
Special Projects
特色数据库
战略所人物
Strategic figures
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于鸿君
于鸿君，经济学博士，北京大学教授，光华管理学院博士生导师，国家社科基金重大招标项目首席专家。国家级精品课程《微观经济学》主持人。 担任北京大学党委副书记，北京大学战略研究所理事长，中国系统科学学会副理事长。 西安交通大学本科毕业后留校任教，后考入北京大学国民经济计划与管理专业攻读硕士学位，获经济学硕士学位后攻读外国经济思想史专业博士，并获经济学博士学位。 于鸿君教授近年来主要从事新制度经济学、国际...
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张圣平
张圣平现任北京大学光华管理学院金融系副教授。在此之前他曾担任过山东大学经济学院，副教授。张圣平教授本科和研究生毕业于山东大学，先后获得数学的学士和硕士学位，他的金融博士学位在南开大学获得。他有多本中文著作，多篇论文发表在国内著名学术期刊上。他目前的研究领域主要在金融经济学、金融监管、非对称信息条件下的金融市场和WTO治理机制。他现在教授货币金融学和金融经济学
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仲为国
研究兴趣包括产业政策与企业创新战略、企业间合作与治理、CEO及高管团队的战略过程、以及发展中国家企业国际化等。20多篇研究作品发表在多种国际和国内著名学术期刊上，包括国际顶级学术期刊。在国际管理学会议上报告论文近20篇，并应邀在多所大学和机构举行学篮员辣术演讲。2007年合作出版《从引进到创新:中国技术政策演进、协同与绩效研究》一书，参编教材1本，主持和参与国家及省部级科研项目10项，其中重点项目2项，获得国...
合作交流
Cooperation and exchange
战略所动态
Strategic Dynamics
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好产品、好渠道、好成果，2025未来数商大会开启数据要素产业新图景
近期，DeepSeek的爆火出圈掀起全民AI思想浪潮。作为AI发展的关键“燃料”，数据的重要性愈发突出。如何进一步激发数据要素的价值？如何应用数据，更好地推动各行各业的发展？
2025/03/21
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王栋教授与合作者在《中国科学院院刊》发文解读地缘技术竞争视角下的中美智库观点
3月19日，北京大学国际关系学院长聘正教授、中外人文交流研究基地执行主任王栋与合作者在《中国科学院院刊》发表封面论文（cover article）《中美地缘技术竞争视角下的中美智库观点及对我国的启示》。浙江外国语学院特聘教授、城市国际化研究院院长，北京大学中外人文交流研究基地学术委员陈溪为本文第一作者，合作者为浙江外国语学院英语语言文化学院邵广妍、南开大学外国语学院胡珈毓、清华大学公共管理学院院长、科技发展与治理研究中心主任，北京大学中外人文交流研究基地学术委员朱旭峰，王栋为本文通讯作者。
2025/04/08
